New Heuristic Model for Optimal CRC Polynomial

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 1, February 2017, pp. 521~525
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i1.pp521-525  521
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
New Heuristic Model for Optimal CRC Polynomial
Ahmed Salih Khirbeet, Ravie Chandren Muniyandi
Research Center for Software Technology and Management, Faculty of Technology and Information Science, University
Kebangsaan Malaysia, Malaysia
Article Info ABSTRACT
Article history:
Received Oct 23, 2016
Revised Dec 14, 2016
Accepted Dec 30, 2016
Cyclic Redundancy Codes (CRCs) are important for maintaining integrity in
data transmissions. CRC performance is mainly affected by the polynomial
chosen. Recent increases in data throughput require a foray into determining
optimal polynomials through software or hardware implementations. Most
CRC implementations in use, offer less than optimal performance or are
inferior to their newer published counterparts. Classical approaches to
determining optimal polynomials involve brute force based searching a
population set of all possible polynomials in that set. This paper evaluates
performance of CRC-polynomials generated with Genetic Algorithms. It then
compares the resultant polynomials, both with and without encryption
headers against a benchmark polynomial.
Keyword:
Cyclic redundancy codes
Data authentication
Data integrity
Genetic algorithms
Optimization Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ahmed Salih Khirbeet,
Research Center for Software Technology and Management,
Faculty of Technology and Information Science,
University Kebangsaan Malaysia, Malaysia.
Email: ahmed.salih89@siswa.ukm.edu.my
1. INTRODUCTION
Cyclic Redundancy Check (CRC) codes are a highly recognized data integrity mechanism in digital
communications for industrial use. They act as a first line of defense in detecting data packet corruption
between two nodes in a communication link [1]. Various CRC implementations exist in different
applications, e.g. from embedded network applications [1] to wireless networks [2], to communication
protocols such as USB 3 [3]. Their wide acceptance is due to their simplicity, good encoding and decoding
performance, and good levels of error detection [4]. As data throughput increases, however, it becomes
important to utilize optimized CRC implementations.
CRC optimization has been attempted from two angles, namely, polynomial selection, and hardware
implementation. The latter is due to the constraints of traditional CRC implementations caused by increasing
data rates. Different hardware implementations have been attempted to cope with this.
An attempt for better polynomial selection from a hardware perspective was described by [5]. They
developed a pipelined implementation of polynomial division which helped form the basis of an effective
CRC. It improved speed, while allowing for data rates from 1 Gbits/s to 4 Gbits/s on field-programmable
gate array (FPGA) implementations with respect to the parallelization level (from 8 to 32 bits). However,
faster data rates, i.e. 5 Gbits/s, were achieved by an 8-bit parallel CRC-32 proposed by [3] to match the high
throughput of USB 3.0. It also proved that an 8-bit parallel CRC-32 was much faster than its serial
counterpart. Despite the improvement, it is overshadowed by research done by [6] who propose a CRC
circuit usable for 10Gbp, utilizing the state-space transformation method.
Improving CRC implementation from a software perspective requires increasing optimization of a
population of polynomials or finding other ways such as reducing the initial search population set of
polynomials. Choosing different polynomials for certain data word lengths and bit lengths offer different
levels of optimization. A polynomial selection process for embedded network applications and a set of

 ISSN: 2088-8708
IJECE Vol. 7, No. 1, February 2017 : 521 – 525
522
"good" general-purpose polynomials has been described by [1]. A collection of 35 new polynomials along
with 13 previously published polynomials that provide good performance for CRCs between 3- and 16-bit for
data word lengths up to 2048 bits were listed. It also discusses a couple of comparison case studies between a
set of current protocols namely USB with ITU (for 5-bit CRCs) and different 8-bit polynomials. The case
studies are used to prove how using different polynomials affect the performance of error detection and how
higher levels of optimization may be achieved by using newer published CRC polynomials. The paper then
provides an exhaustive survey of CRC polynomials from 3- to 15-bit along with a discussion of 16-bit
polynomials. The paper also defines "good" polynomials as those achieving the maximum Hamming
Distance for the longest data word along with other considerations. Despite the comprehensive nature of this
research, CRC codes beyond 16 parity bits were avoided due to the large number of possibilities that had to
be investigated. However, certain classes of 24- and 32-bit CRC codes were investigated by [7], whereas a
comprehensive search for CRC codes with 32 parity bits was undertaken by [8].
Similar to [1], an analysis of CRC code standards, namely, CRC-12, ANSI and CCI7T X.25, proved
that the standards were not on par with the best possible CRC implementations and that the situation seemed
unlikely to change due to economic concerns [4]. This coupled with the use of CRCs in high-throughput
applications such as USB 3 [3], has made parallel CRC circuitry design, an important area of research [9].
Performance of CRC implementations created by polynomials with a degree of 16 and above for
error detection in communication systems was evaluated by [4]. The properties of minimum distance,
"properness" and undetected error probability for binary symmetric channels (BSC) are calculated and
compared with existing standards.
The core of CRC implementations depends on the polynomial chosen. Despite their importance in
ensuring data integrity, a comprehensive research disclosed that most published CRC polynomials are either
optimal when limited to certain message lengths or inferior to alternatives. The latter is supported by the fact
that many applications utilize CRCs that offer much less error detection capability than possible for a certain
CRC bit value. This may also be supported by the lack of "tools and data tables" for measuring polynomial
performance [1].
With regards to polynomial selection, the current landscape suffers from three issues: (1) lacunae
exist in the current published polynomials set (2) no specific guidance exists on the viability of certain
polynomials in certain contexts, and (3) lacunae with respect to published quantitative analyses [1]. Research
has shown that varying polynomials within a certain size as well as varying the size, produces different
results with respect to error detecting performance [4].
2. METHODOLOGY
With regards to embedded networks, an attribute of concern with CRC implementation is the
Hamming Distance (HD) [1]. HD is the least bit inversions that have to be integrated into a message to
generate an error that is "undetectable by that message's CRC-based Frame Check Sequence". The
probability of such a data corruption occurring is small but nonetheless, the least number of bit inversions in
order to achieve the aforementioned undetectable errors is crucial in CRC polynomial design [1]; size of bits
and data words are also to be considered since they affect error-detection performance [4].
Connected with HD is the attribute, Hamming Weight (HW). For a HW of N, N is the number of
undetected error probabilities by a CRC implementation with respect to a specific polynomial. A collection
of HWs captures performance for different numbers of bits corrupted in a message at a particular data word
length. The first non-zero Hamming weight determines a code’s HD.
The goal of finding a good CRC polynomial is to ensure optimization, i.e., to maximize the HD and
minimize the HW. The classical way to find such polynomials is to try all possible cases of polynomials and
data bit errors (1-, 2-, 3-, message length errors), and then select the optimal one. This method is suitable for
small values of data word lengths and polynomial degree. However, for larger values, computational
complexity increases and thus makes solutions impossible on today's computers.
We propose solving the issue of reducing computational complexity by using the concept of Genetic
Algorithms (GA), thus treating the problem as an optimization problem. GA is a type of evolutionary
computation method, but has no agreed upon definition which differentiates it from other evolution-
computation methods. However, there are some features which consistently present themselves in GA
methods: “populations of chromosomes, selection according to fitness, crossover to produce new offspring,
and random mutation of new offspring.”[10]. Also, genetic algorithms have been applied in various
applications in communication [11], [12].
GAs help reduce the population of a certain problem set by performing a directed search [13]. This
is in contrast to the classical method, which is random in nature and requires going through all members of
the problem set's population.

IJECE ISSN: 2088-8708 
New Heuristic Model for Optimal CRC Polynomial (Ahmed Salih Khirbeet)
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GA allows movement from one set of data represented in binary ("chromosomes") in the search
space to a new one by performing a type of "natural selection", as is present in nature. It utilizes genetics-
inspired operators: crossover, mutation, and inversion. Similar to the natural inspiration it was derived from,
each "chromosome" consists of "genes". "Genes" in turn represent an instance of a certain "allele", i.e., 0
or 1. "Chromosomes" are selected on their fitness to reproduce; fitter chromosomes on average produce more
offspring. The Operators then define how new "chromosomes" are to be selected until an optimal solution is
reached [10].
In relation to the case at hand, the highest possible degree for any polynomial is n. Therefore, the
length of each chromosome is n and each chromosome is padded with “1”s which represent the constant in
the polynomial.
HD has been used as a constraint, so each chromosome that does not satisfy the constraint is
rejected. Before starting a search process, the user must determine bit error cases (from 1 to the required
value of bit errors), the maximum number of combinations for each case, and the message length. Because
binary representation has been used, the values' lower bound is zero (0) while the higher bound is one (1).
The values are always integers (GF(2)). The target of the optimization process is to minimize the following
function:
∑
The Fitness Value (FV) equation clarifies that increasing the number of used combinations makes
the resultant solution nearer to the best solution, but not without increasing the computational complexity.
Data integrity of the data packets transferred can be ensured by using encryption. One of secure
cipher operation's principles is diffusion. It is satisfied when, if a single bit of the plain text is changed, every
bit of the cipher text changes with a probability of 0.5 and vice versa. This is useful for the scenario at hand
as any error that occurs in the message during transmission will cause a large number of errors in the
message after decryption which in turn helps the next stage (CRC detector) to reduce the number of
undetected errors. The number of undetected errors can be further reduced by ensuring each data packet
contains a header which is known to both the sender and receiver. Thus, if the error is undetected using FCS,
it can be detected by checking for changes in the header after decryption.
The Advanced Encryption Standard (AES) specification will be used for encryption functions
relating to headers. The specification [14] defines it as a block cipher which can be used both ways, i.e., to
encrypt information into ciphertext and also to decrypt the resultant ciphertext back into its original form. It
is a symmetric-key algorithm, which means that the same key is used for both encryption and decryption
functions. It can encrypt and decrypt data in 128-bit blocks by using 128, 192, and 256-bit cryptographic
keys. We will be using a 128-bit cryptographic key for our application.
3. RESULTS AND DISSCUSSION
To validate the proposed method, we conducted experiments using the MATLAB environment with
its built-in GA toolbox. The first experiment consisted of data packets of 48 bits, and where the highest
degree of the generator polynomial was 16. Bit error cases ranged from 1 to 6f bits, and for each case, 10,000
combinations were tested at most.
The HD was chosen to be at minimum, 6. The benchmark chosen for this experiment was the
polynomial found by [4], with a hex value of 0xC86C. An analysis by [1] found it to the best amongst many
others for a data word size of 48 bits. Results are shown in Table 1.
Table 1. Resultant Polynomial Vs [4], Tested With 10,000 Combinations
HD Polynomial 1 bit 2 bits 3 bits 4 bits 5 bits 6 bits No. of Undetected Errors
Resultant 0x8411 5 0 0 0 0 183 1267 1450
[4] 0xC86C 6 0 0 0 0 0 2191 2191
Although the resultant polynomial has a lower HD than [4], the sum of undetected errors in 5-bit
and 6-bit cases is lower than undetected errors in [4] for 6 bits. As previously mentioned, increasing the
maximum number of combinations makes the solution nearer to the best solution. Therefore, another
experiment was conducted; this time, making the maximum number of combinations, 300,000. The results
are summarized in Table 2.

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IJECE Vol. 7, No. 1, February 2017 : 521 – 525
524
Table 2. Resultant Polynomial Vs [4], Tested With 300,000 Combinations
HD Polynomial 1 bit 2 bits 3 bits 4 bits 5 bits 6 bits No. of Undetected Errors
Resultant 0x9947 5 0 0 0 0 57 1163 1220
[4] 0xC86C 6 0 0 0 0 0 2191 2191
It is clear that the new resultant polynomial is able to detect more error cases than the previous one,
and thus [4]'s as well. The number of undetected error probabilities has decreased significantly from the
previous experiment, resulting in 57 compared with 183 from the previous experiment, for 5-bits. The
improvement in 6-bits was not significant but nonetheless still better by 104 undetected error probabilities.
In order to validate the effect of encryption in lowering the number of undetected errors, another
experiment was conducted. This time, a data word size of 112 was used and the highest possible polynomial
degree was 16. Bit error cases ranged from 1 to 6 bits, and 400,000 combinations at most, were tested for
each case. The HD constraint was left as is, with a minimum value of 6.
Table 3 shows the resultant polynomial, the number of undetected errors, the number of undetected
errors with encryption, and the number of undetected errors with encryption along with a header of one-byte
size, where 4 million combinations of 6-bit errors have been randomly chosen and tested. The data word size
was 112 bit. The benefit of adding encryption as well as a header can be clearly seen. The number of
undetected error probabilities without encryption and a header was 141. Adding encryption reduced it to 60
and adding a header along with encryption decreased it even further, enabling the CRC implementation to
catch all possible errors.
Table 3. No. Of Undetected Error Probabilities for the Polynomial 0x8948
Polynomial CRC CRC and Encryption CRC and Encryption with Header
0x8948 141 60 0
4. CONCLUSION AND FUTURE WORK
Our work showed how using a GA approach decreased the number of possible solution candidates
for finding an optimal polynomial for use in a CRC implementation. We compared the resultant polynomials
against a benchmark [4] for a data word length of 48 bits. We also evaluated the effect of encryption on data
headers in a CRC implementation; we found that encryption improves performance of a CRC
implementation.
Our evaluation, however, is not comprehensive which leaves room for future work. A more
thorough evaluation of using GA for different data word lengths and CRC bit length would add strength to
the proposal at hand. Similarly, a thorough benchmark analysis of resultant polynomials using GA for the
various data word lengths and CRC bit length would prove the validity of our method across a wider scope of
CRC implementations.
ACKNOWLEDGEMENTS
The researchers wish to thank Universiti Kebangsaan Malaysia (UKM) for supporting this work by
research grants: Dana Impak Perdana (DIP-2014-037).
REFERENCES
[1] P. Koopman and T. Chakravarty, “Cyclic Redundancy Code (CRC) Polynomial Selection For Embedded
Networks”, Int. Conf. Dependable Syst. Networks, 2004, pp. 1–11, 2004.
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communications in 802.11 wireless networks”, in IEEE conference on communications and network security (cns),
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[3] Y. Wu and Y. Qiu, “The 8-bit parallel crc-32 research and implementation in usb 3.0”, in International conference
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[4] T. Baicheva, S. Dodunekov, and P. Kazakov, “Undetected error probability performance of cyclic redundancy-
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detecting codes”, in The 13th international conference on Microelectronics, 2001, vol. 13, pp. 133–136.

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[6] J.S. Lin, C.K. Lee, M.D. Shieh, and J.H. Chen, “High-speed crc design for 10 gbps applications”, in IEEE
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[13] S. Sivanandam and S. Deepa, Introduction to genetic algorithms. Springer Berlin Heidelberg, 2007.
[14] “Specification for the advanced encryption standard (AES),” Fed. Inf. Process. Stand. Publ., 2001.
BIOGRAPHIES OF AUTHORS
Ahmed Salih Khirbeet
Research Center for Software Technology and Management (SOFTAM)
Faculty of Technology and Information Science
University Kebangsaan Malaysia
ahmed.salih89@siswa.ukm.edu.my
Ravie Chandren Muniyandi
Research Center for Software Technology and Management (SOFTAM)
Faculty of Technology and Information Science
University Kebangsaan Malaysia.
ravie@ukm.edu.my

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